Prefrontal High Gamma in ECoG Tags Periodicity of Musical Rhythms in Perception and Imagination

Rhythmic auditory stimuli are known to elicit matching activity patterns in neural populations. Furthermore, recent research has established the particular importance of high-gamma brain activity in auditory processing by showing its involvement in auditory phrase segmentation and envelope tracking. Here, we use electrocorticographic (ECoG) recordings from eight human listeners to see whether periodicities in high-gamma activity track the periodicities in the envelope of musical rhythms during rhythm perception and imagination. Rhythm imagination was elicited by instructing participants to imagine the rhythm to continue during pauses of several repetitions. To identify electrodes whose periodicities in high-gamma activity track the periodicities in the musical rhythms, we compute the correlation between the autocorrelations (ACCs) of both the musical rhythms and the neural signals. A condition in which participants listened to white noise was used to establish a baseline. High-gamma autocorrelations in auditory areas in the superior temporal gyrus and in frontal areas on both hemispheres significantly matched the autocorrelations of the musical rhythms. Overall, numerous significant electrodes are observed on the right hemisphere. Of particular interest is a large cluster of electrodes in the right prefrontal cortex that is active during both rhythm perception and imagination. This indicates conscious processing of the rhythms\u27 structure as opposed to mere auditory phenomena. The autocorrelation approach clearly highlights that high-gamma activity measured from cortical electrodes tracks both attended and imagined rhythms

Duration of disease does not equally influence all aspects of quality of life in Parkinson’s disease

Health related quality of life (HRQoL) is negatively impacted in patients suffering from Parkinsons disease (PD). For the specific components that comprise HRQoL, the relationship between clinical variables, such as disease duration, is not fully characterized. In this cross-sectional study (n=302), self-reported HRQoL on the Parkinsons Disease Questionnaire (PDQ-39) was evaluated as a global construct as well as individual subscale scores. HRQoL was compared in three groups: those within 5years of diagnosis, those within 6-10years of diagnosis, and those greater than 11years since diagnosis. Non-parametric analyses revealed lower HRQoL with increasing disease duration when assessed as a global construct. However, when subscales were evaluated, difficulties with bodily discomfort and cognitive complaints were comparable in individuals in the 1-5years and 6-10year duration groups. Exploratory regression analyses suggested disease duration does explain unique variance in some subscales, even after controlling for Hoehn and Yahr stage and neuropsychiatric features. Our findings show that HRQoL domains in PD patients are affected differentially across the duration of the disease. Clinicians and researchers may need to tailor interventions intended to improve HRQoL at different domains as the disease progresses

Discrimination of Overt, Mouthed, and Imagined Speech Activity using Stereotactic EEG

Recent studies have demonstrated that it is possible to decode and synthesize acoustic speech directly from intracranial measurements of brain activity. A current major challenge is to extend the efficacy of this decoding to imagined speech processes toward the development of a practical speech neuroprosthesis for the disabled. The present study used intracranial brain recordings from participants that performed a speaking task consisting of overt, mouthed, and imagined speech trials. In order to better elucidate the unique neural features that contribute to the discrepancies between overt and imagined model performance, rather than directly comparing the performance of speech decoding models trained on respective speaking modes, this study developed and trained models that use neural data to discriminate between pairs of speaking modes. The results further support that, while there exists a common neural substrate across speech modes, there are also unique neural processes that differentiate speech modes

Hybrid fNIRS-EEG based classification of auditory and visual perception processes

For multimodal Human-Computer Interaction (HCI), it is very useful to identify the modalities on which the user is currently processing information. This would enable a system to select complementary output modalities to reduce the user\u27s workload. In this paper, we develop a hybrid Brain-Computer Interface (BCI) which uses Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) to discriminate and detect visual and auditory stimulus processing. We describe the experimental setup we used for collection of our data corpus with 12 subjects. On this data, we performed cross-validation evaluation, of which we report accuracy for different classification conditions. The results show that the subject-dependent systems achieved a classification accuracy of 97.8% for discriminating visual and auditory perception processes from each other and a classification accuracy of up to 94.8% for detecting modality-specific processes independently of other cognitive activity. The same classification conditions could also be discriminated in a subject-independent fashion with accuracy of up to 94.6 and 86.7%, respectively. We also look at the contributions of the two signal types and show that the fusion of classifiers using different features significantly increases accuracy

Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework

Speech Neuroprostheses have the potential to enable communication for people with dysarthria or anarthria. Recent advances have demonstrated high-quality text decoding and speech synthesis from electrocorticographic grids placed on the cortical surface. Here, we investigate a less invasive measurement modality in three participants, namely stereotactic EEG (sEEG) that provides sparse sampling from multiple brain regions, including subcortical regions. To evaluate whether sEEG can also be used to synthesize high-quality audio from neural recordings, we employ a recurrent encoder-decoder model based on modern deep learning methods. We find that speech can indeed be reconstructed with correlations up to 0.8 from these minimally invasive recordings, despite limited amounts of training data

Decoding executed and imagined grasping movements from distributed non-motor brain areas using a Riemannian decoder

Using brain activity directly as input for assistive tool control can circumventmuscular dysfunction and increase functional independence for physically impaired people. The motor cortex is commonly targeted for recordings, while growing evidence shows that there exists decodable movement-related neural activity outside of the motor cortex. Several decoding studies demonstrated significant decoding from distributed areas separately. Here, we combine information from all recorded non-motor brain areas and decode executed and imagined movements using a Riemannian decoder. We recorded neural activity from 8 epilepsy patients implanted with stereotactic-electroencephalographic electrodes (sEEG), while they performed an executed and imagined grasping tasks. Before decoding, we excluded all contacts in or adjacent to the central sulcus. The decoder extracts a low-dimensional representation of varying number of components, and classified move/no-move using a minimum-distance-to-geometric-mean Riemannian classifier. We show that executed and imagined movements can be decoded from distributed non-motor brain areas using a Riemannian decoder, reaching an area under the receiver operator characteristic of 0.83 ± 0.11. Furthermore, we highlight the distributedness of the movement-related neural activity, as no single brain area is the main driver of performance. Our decoding results demonstrate a first application of a Riemannian decoder on sEEG data and show that it is able to decode from distributed brain-wide recordings outside of the motor cortex. This brief report highlights the perspective to explore motor-related neural activity beyond the motor cortex, as many areas contain decodable information